Programmable hearing aids offer the possibility of making choices for the values of multiple parameters, hence allowing for a very wide range of electroacoustic responses capable of accommodating many different kinds of hearing impairments. The audiologist/hearing aid practitioner using audiometric measurements decides on the objectives/targets that the hearing device should match in order to compensate for the impairment.
With the advent of programmable hearing instruments, it has become possible to achieve near optimal matching between the electroacoustic responses and the targets. The targets are derived from prescriptive procedures, based on theoretical or empirical considerations, originally proposed for linear nonprogrammable hearing devices. Lately, new prescriptive formulas intended for nonlinear programmable devices such as DSLi/o and FIG. 6 are being used. Nevertheless, their targets are based on artificial listening environments (i.e., speech weighted noise) and do not adequately characterize hearing aid performance in realistic environments.
The audiologist is then often faced with user complaints that reflect the performance of the device in everyday environments and he/she then must adjust the operating parameters of the device with suboptimal tools and methods. Setting those parameters one at a time (e.g., AGC for the low channel, Gain for the high channel) is a suboptimal procedure because there is a great degree of interdependence between the parameters. Furthermore, the user responses and complaints are imprecise and uncertain and they need to be deciphered by the audiologist.
Finally, even tools that allow for an automatic mechanism for deciphering the user responses, such as the fuzzy logic device described in U.S. Pat. No. 5,606,620, are not optimal because they directly control the individual parameters of the device. Because of this direct control, many contradictory demands on setting the parameters are not adequately resolved by the inherent capabilities of the fuzzy logic. Furthermore, the said system does not provide for an efficient mechanism for incorporating learning from successful fittings other than the manual entry of fuzzy rules.